To achieve early perception and accurate prediction of flatness quality, a partial least squares–particle swarm optimization–multi-output support vector regression (PLS-PSO-MSVR) is proposed. Firstly, we parameterized the flatness and used it as an evaluation indicator for flatness. Then, the prediction model was constructed using multi-output support vector regression (MSVR). In the modeling process, particle swarm optimization is used to optimize the parameters. To overcome the problem of information redundancy, reduce data dimensions to reduce computational time, and improve the prediction performance of the algorithm, this paper combines partial least squares and PSO-MSVR to achieve accurate prediction of the flatness features. Finally, the actual industrial process data from the hot rolling 1580 production line was used for validation, and the predicted performance was evaluated using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R²). MAE decreased to 0.15, MSE decreased to 0.038, and RMSE decreased to 0.195. The R² approaches 1, indicating excellent model fit. This study achieves accurate prediction of the flatness characteristic coefficient, which not only enhances the diagnostic efficiency of steel flatness quality but also helps avoid unnecessary economic losses. Moreover, the prediction model provides a reliable basis for flatness control, offering operators a user-friendly reference tool. This approach compensates for the time lag inherent in the original system and contributes to improved accuracy in flatness control.

The torsional behavior of an extruded AZ31 magnesium alloy was investigated by combined axial-torsion mechanical testing with different stress ratios. The SEM-EBSD was used to analyze the microstructure and texture evolution of deformed samples. The results indicate that the axial tension results in a concave-down shape of shear stress–strain curves, while a concave-up shape after yielding is presented during combined compression-torsion loading due to twinning mechanism. Compared to pure shear, the yield strength decreases by 7 MPa and shear strain increases by 1% under σ:τ = −1:1 with same shear stress. Due to the Swift effect, a strain partitioning is displayed for axial strain during combined tension-torsion loading with low stress ratio. The twin volume fraction is 90% under σ:τ = −1:1, and the local dislocation density with a KAM value of 1.1 is maximum under σ:τ = 2:1. The primary twin type is {10-12} twins with axial compression. The main deformation mode changes from basal slip to prismatic slip with increase in axial tension stress. Both basal slip and twinning are activated and the interaction between dislocation slip and twinning contributes to the complex strain hardening behavior during combined compression-torsion loading.